ChannelNet [1]} treats the LS estimated values at pilot sub-carriers in an OFDM frame as a low-resolution 2D image and develops a CNN-based image enhancement technique to increase the resolution to obtain accurate channel estimates at data sub-carriers. Its performance is sensitive to the SNR conditions, which demand SNR-specific models resulting in high reconfigurable time and large on-chip memory. This model is further enhanced [2]} by replacing the two CNN models with a single deep residual neural network (ReEsNet) to reduce its complexity and improve performance. Interpolation-ResNet in [3]} replaces the transposed convolution layer of ReEsNet with a bilinear interpolation block to reduce the complexity further and make the network flexible for any pilot pattern. The computational complexity and memory requirements of these models are high, even higher than the complexity of PHY. In this work, we focus on augmenting the conventional channel estimation with DL instead of DL-based channel estimation [4]}, [5]}.
